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Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between agents.
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Learning when to communicate at scale in multiagent cooperative and competitive tasks
Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar. 2018 · 2018
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Ensemble adversarial training: attacks and defenses. In ICLR
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Tarmac: Targeted multi-agent communication. In ICML . 1538–1546
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In ICML . 3040–3049
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Learning to schedule communication in multi-agent reinforcement learning
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A generalized training approach for multiagent learning. In ICLR
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PyTorch: An Imperative Style, High-Performance Deep Learning Library. In NeurIPS . 8026–8037
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Rupert Mitchell, Jan Blumenkamp, and Amanda Prorok. 2020 · 2020
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Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management. In AAMAS . 1278–1286
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Learning efficient multi-agent communication: An information bottleneck approach. In ICML . 9908–9918
Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, and Zinovi Rabinovich. 2020 · 2020
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Succinct and robust multi-agent communication with temporal message control
Sai Qian Zhang, Jieyu Lin, and Qi Zhang. 2020b · 2020
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Correcting Experience Replay for Multi-Agent Communication
Sanjeevan Ahilan and Peter Dayan. 2021 · 2021
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Communication in multi-Agent reinforcement learning: Intention Sharing
Woojun Kim, Jongeui Park, and Youngchul Sung. 2021 · 2021
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Adversarial attacks on multi-agent communication
James Tu, Tsunhsuan Wang, Jingkang Wang, Sivabalan Manivasagam, Mengye Ren, and Raquel Urtasun. 2021 · 2021
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Transferable environment poisoning: Training-time attack on reinforcement learning. In AAMAS . 1398–1406
Hang Xu, Rundong Wang, Lev Raizman, and Zinovi Rabinovich. 2021 · 2021
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Robust reinforcement learning on state observations with learned optimal adversary. In ICLR
Huan Zhang, Hongge Chen, Duane S Boning, and Cho-Jui Hsieh. 2021 · 2021
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